{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "\n",
    "# 绘图设置\n",
    "sns.set()\n",
    "plt.rcParams['figure.dpi'] = 200  # 我的屏幕太tm大了\n",
    "plt.rcParams['font.sans-serif'] = 'Arial Unicode MS'  # 设置中文字体\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jv/x07wnb891fq0zvyzm614kpg80000gn/T/ipykernel_31494/3863808386.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df['new'+str(i)+'*'+str(j)] = df['v_'+str(i)]*df['v_'+str(j)]\n",
      "/var/folders/jv/x07wnb891fq0zvyzm614kpg80000gn/T/ipykernel_31494/3863808386.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df['new'+str(i)+'+'+str(j)] = df['v_'+str(i)]+df['v_'+str(j)]\n",
      "/var/folders/jv/x07wnb891fq0zvyzm614kpg80000gn/T/ipykernel_31494/3863808386.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df['new' + str(i) + '*power'] = df['v_' + str(i)] * df['power']\n",
      "/var/folders/jv/x07wnb891fq0zvyzm614kpg80000gn/T/ipykernel_31494/3863808386.py:32: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df['new'+str(i)+'-'+str(j)] = df['v_'+str(i)]-df['v_'+str(j)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.410343 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 89260\n",
      "[LightGBM] [Info] Number of data points in the train set: 120000, number of used features: 357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.9/site-packages/lightgbm/basic.py:1433: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "/opt/miniconda3/lib/python3.9/site-packages/lightgbm/basic.py:1245: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning('{} in param dict is overridden.'.format(cat_alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20\n",
      "[LightGBM] [Info] Start training from score 8.086411\n",
      "[1]\tvalid_0's l1: 0.979104\n",
      "Training until validation scores don't improve for 300 rounds\n",
      "[2]\tvalid_0's l1: 0.961878\n",
      "[3]\tvalid_0's l1: 0.945204\n",
      "[4]\tvalid_0's l1: 0.928596\n",
      "[5]\tvalid_0's l1: 0.912734\n",
      "[6]\tvalid_0's l1: 0.896879\n",
      "[7]\tvalid_0's l1: 0.881591\n",
      "[8]\tvalid_0's l1: 0.86688\n",
      "[9]\tvalid_0's l1: 0.852324\n",
      "[10]\tvalid_0's l1: 0.838104\n",
      "[11]\tvalid_0's l1: 0.824385\n",
      "[12]\tvalid_0's l1: 0.810858\n",
      "[13]\tvalid_0's l1: 0.797662\n",
      "[14]\tvalid_0's l1: 0.784737\n",
      "[15]\tvalid_0's l1: 0.771979\n",
      "[16]\tvalid_0's l1: 0.759662\n",
      "[17]\tvalid_0's l1: 0.747361\n",
      "[18]\tvalid_0's l1: 0.735417\n",
      "[19]\tvalid_0's l1: 0.723391\n",
      "[20]\tvalid_0's l1: 0.711648\n",
      "[21]\tvalid_0's l1: 0.70038\n",
      "[22]\tvalid_0's l1: 0.689384\n",
      "[23]\tvalid_0's l1: 0.67871\n",
      "[24]\tvalid_0's l1: 0.668055\n",
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      "[27]\tvalid_0's l1: 0.637665\n",
      "[28]\tvalid_0's l1: 0.627977\n",
      "[29]\tvalid_0's l1: 0.618401\n",
      "[30]\tvalid_0's l1: 0.609162\n",
      "[31]\tvalid_0's l1: 0.600095\n",
      "[32]\tvalid_0's l1: 0.591\n",
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      "[42]\tvalid_0's l1: 0.511751\n",
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      "[308]\tvalid_0's l1: 0.145546\n",
      "[309]\tvalid_0's l1: 0.145424\n",
      "[310]\tvalid_0's l1: 0.145306\n",
      "[311]\tvalid_0's l1: 0.145191\n",
      "[312]\tvalid_0's l1: 0.145065\n",
      "[313]\tvalid_0's l1: 0.144956\n",
      "[314]\tvalid_0's l1: 0.144818\n",
      "[315]\tvalid_0's l1: 0.144684\n",
      "[316]\tvalid_0's l1: 0.144582\n",
      "[317]\tvalid_0's l1: 0.144467\n",
      "[318]\tvalid_0's l1: 0.144374\n",
      "[319]\tvalid_0's l1: 0.144235\n",
      "[320]\tvalid_0's l1: 0.144142\n",
      "[321]\tvalid_0's l1: 0.144031\n",
      "[322]\tvalid_0's l1: 0.143908\n",
      "[323]\tvalid_0's l1: 0.143831\n",
      "[324]\tvalid_0's l1: 0.143756\n",
      "[325]\tvalid_0's l1: 0.143669\n",
      "[326]\tvalid_0's l1: 0.143563\n",
      "[327]\tvalid_0's l1: 0.143491\n",
      "[328]\tvalid_0's l1: 0.14339\n",
      "[329]\tvalid_0's l1: 0.143319\n",
      "[330]\tvalid_0's l1: 0.143244\n",
      "[331]\tvalid_0's l1: 0.143124\n",
      "[332]\tvalid_0's l1: 0.143039\n",
      "[333]\tvalid_0's l1: 0.142932\n",
      "[334]\tvalid_0's l1: 0.142814\n",
      "[335]\tvalid_0's l1: 0.142758\n",
      "[336]\tvalid_0's l1: 0.142674\n",
      "[337]\tvalid_0's l1: 0.14257\n",
      "[338]\tvalid_0's l1: 0.142482\n",
      "[339]\tvalid_0's l1: 0.142368\n",
      "[340]\tvalid_0's l1: 0.142258\n",
      "[341]\tvalid_0's l1: 0.142151\n",
      "[342]\tvalid_0's l1: 0.142098\n",
      "[343]\tvalid_0's l1: 0.141974\n",
      "[344]\tvalid_0's l1: 0.141883\n",
      "[345]\tvalid_0's l1: 0.141772\n",
      "[346]\tvalid_0's l1: 0.141678\n",
      "[347]\tvalid_0's l1: 0.141588\n",
      "[348]\tvalid_0's l1: 0.141515\n",
      "[349]\tvalid_0's l1: 0.141392\n",
      "[350]\tvalid_0's l1: 0.141289\n",
      "[351]\tvalid_0's l1: 0.141209\n",
      "[352]\tvalid_0's l1: 0.141119\n",
      "[353]\tvalid_0's l1: 0.141057\n",
      "[354]\tvalid_0's l1: 0.14099\n",
      "[355]\tvalid_0's l1: 0.140889\n",
      "[356]\tvalid_0's l1: 0.140815\n",
      "[357]\tvalid_0's l1: 0.140705\n",
      "[358]\tvalid_0's l1: 0.140643\n",
      "[359]\tvalid_0's l1: 0.140569\n",
      "[360]\tvalid_0's l1: 0.140512\n",
      "[361]\tvalid_0's l1: 0.140432\n",
      "[362]\tvalid_0's l1: 0.140343\n",
      "[363]\tvalid_0's l1: 0.140279\n",
      "[364]\tvalid_0's l1: 0.140186\n",
      "[365]\tvalid_0's l1: 0.140104\n",
      "[366]\tvalid_0's l1: 0.140021\n",
      "[367]\tvalid_0's l1: 0.13993\n",
      "[368]\tvalid_0's l1: 0.139845\n",
      "[369]\tvalid_0's l1: 0.139796\n",
      "[370]\tvalid_0's l1: 0.139719\n",
      "[371]\tvalid_0's l1: 0.139667\n",
      "[372]\tvalid_0's l1: 0.139615\n",
      "[373]\tvalid_0's l1: 0.139556\n",
      "[374]\tvalid_0's l1: 0.139472\n",
      "[375]\tvalid_0's l1: 0.139401\n",
      "[376]\tvalid_0's l1: 0.139311\n",
      "[377]\tvalid_0's l1: 0.139248\n",
      "[378]\tvalid_0's l1: 0.139172\n",
      "[379]\tvalid_0's l1: 0.139087\n",
      "[380]\tvalid_0's l1: 0.138993\n",
      "[381]\tvalid_0's l1: 0.138911\n",
      "[382]\tvalid_0's l1: 0.138841\n",
      "[383]\tvalid_0's l1: 0.138778\n",
      "[384]\tvalid_0's l1: 0.138718\n",
      "[385]\tvalid_0's l1: 0.138672\n",
      "[386]\tvalid_0's l1: 0.138627\n",
      "[387]\tvalid_0's l1: 0.138557\n",
      "[388]\tvalid_0's l1: 0.138516\n",
      "[389]\tvalid_0's l1: 0.138466\n",
      "[390]\tvalid_0's l1: 0.138386\n",
      "[391]\tvalid_0's l1: 0.138339\n",
      "[392]\tvalid_0's l1: 0.138254\n",
      "[393]\tvalid_0's l1: 0.138176\n",
      "[394]\tvalid_0's l1: 0.138098\n",
      "[395]\tvalid_0's l1: 0.138014\n",
      "[396]\tvalid_0's l1: 0.137956\n",
      "[397]\tvalid_0's l1: 0.137895\n",
      "[398]\tvalid_0's l1: 0.137825\n",
      "[399]\tvalid_0's l1: 0.137797\n",
      "[400]\tvalid_0's l1: 0.137739\n",
      "[401]\tvalid_0's l1: 0.137698\n",
      "[402]\tvalid_0's l1: 0.137627\n",
      "[403]\tvalid_0's l1: 0.137594\n",
      "[404]\tvalid_0's l1: 0.137544\n",
      "[405]\tvalid_0's l1: 0.137493\n",
      "[406]\tvalid_0's l1: 0.137435\n",
      "[407]\tvalid_0's l1: 0.137383\n",
      "[408]\tvalid_0's l1: 0.137354\n",
      "[409]\tvalid_0's l1: 0.137328\n",
      "[410]\tvalid_0's l1: 0.137266\n",
      "[411]\tvalid_0's l1: 0.137196\n",
      "[412]\tvalid_0's l1: 0.137148\n",
      "[413]\tvalid_0's l1: 0.137075\n",
      "[414]\tvalid_0's l1: 0.137018\n",
      "[415]\tvalid_0's l1: 0.136978\n",
      "[416]\tvalid_0's l1: 0.136896\n",
      "[417]\tvalid_0's l1: 0.136823\n",
      "[418]\tvalid_0's l1: 0.136766\n",
      "[419]\tvalid_0's l1: 0.136735\n",
      "[420]\tvalid_0's l1: 0.136689\n",
      "[421]\tvalid_0's l1: 0.136652\n",
      "[422]\tvalid_0's l1: 0.136601\n",
      "[423]\tvalid_0's l1: 0.136558\n",
      "[424]\tvalid_0's l1: 0.136495\n",
      "[425]\tvalid_0's l1: 0.136407\n",
      "[426]\tvalid_0's l1: 0.136345\n",
      "[427]\tvalid_0's l1: 0.136298\n",
      "[428]\tvalid_0's l1: 0.136252\n",
      "[429]\tvalid_0's l1: 0.136205\n",
      "[430]\tvalid_0's l1: 0.136149\n",
      "[431]\tvalid_0's l1: 0.136086\n",
      "[432]\tvalid_0's l1: 0.13604\n",
      "[433]\tvalid_0's l1: 0.135975\n",
      "[434]\tvalid_0's l1: 0.135935\n",
      "[435]\tvalid_0's l1: 0.135886\n",
      "[436]\tvalid_0's l1: 0.135842\n",
      "[437]\tvalid_0's l1: 0.135771\n",
      "[438]\tvalid_0's l1: 0.135705\n",
      "[439]\tvalid_0's l1: 0.135659\n",
      "[440]\tvalid_0's l1: 0.135572\n",
      "[441]\tvalid_0's l1: 0.135508\n",
      "[442]\tvalid_0's l1: 0.135478\n",
      "[443]\tvalid_0's l1: 0.135444\n",
      "[444]\tvalid_0's l1: 0.135404\n",
      "[445]\tvalid_0's l1: 0.135357\n",
      "[446]\tvalid_0's l1: 0.135312\n",
      "[447]\tvalid_0's l1: 0.135277\n",
      "[448]\tvalid_0's l1: 0.135234\n",
      "[449]\tvalid_0's l1: 0.135142\n",
      "[450]\tvalid_0's l1: 0.135093\n",
      "[451]\tvalid_0's l1: 0.135034\n",
      "[452]\tvalid_0's l1: 0.134993\n",
      "[453]\tvalid_0's l1: 0.134957\n",
      "[454]\tvalid_0's l1: 0.134897\n",
      "[455]\tvalid_0's l1: 0.134864\n",
      "[456]\tvalid_0's l1: 0.134792\n",
      "[457]\tvalid_0's l1: 0.134759\n",
      "[458]\tvalid_0's l1: 0.134734\n",
      "[459]\tvalid_0's l1: 0.134684\n",
      "[460]\tvalid_0's l1: 0.134647\n",
      "[461]\tvalid_0's l1: 0.134605\n",
      "[462]\tvalid_0's l1: 0.134574\n",
      "[463]\tvalid_0's l1: 0.1345\n",
      "[464]\tvalid_0's l1: 0.134466\n",
      "[465]\tvalid_0's l1: 0.13444\n",
      "[466]\tvalid_0's l1: 0.134396\n",
      "[467]\tvalid_0's l1: 0.134365\n",
      "[468]\tvalid_0's l1: 0.134307\n",
      "[469]\tvalid_0's l1: 0.134254\n",
      "[470]\tvalid_0's l1: 0.134198\n",
      "[471]\tvalid_0's l1: 0.134137\n",
      "[472]\tvalid_0's l1: 0.134089\n",
      "[473]\tvalid_0's l1: 0.134039\n",
      "[474]\tvalid_0's l1: 0.134001\n",
      "[475]\tvalid_0's l1: 0.133975\n",
      "[476]\tvalid_0's l1: 0.133922\n",
      "[477]\tvalid_0's l1: 0.133889\n",
      "[478]\tvalid_0's l1: 0.133865\n",
      "[479]\tvalid_0's l1: 0.133819\n",
      "[480]\tvalid_0's l1: 0.133782\n",
      "[481]\tvalid_0's l1: 0.133746\n",
      "[482]\tvalid_0's l1: 0.133693\n",
      "[483]\tvalid_0's l1: 0.133641\n",
      "[484]\tvalid_0's l1: 0.133588\n",
      "[485]\tvalid_0's l1: 0.133532\n",
      "[486]\tvalid_0's l1: 0.133483\n",
      "[487]\tvalid_0's l1: 0.133453\n",
      "[488]\tvalid_0's l1: 0.133423\n",
      "[489]\tvalid_0's l1: 0.133381\n",
      "[490]\tvalid_0's l1: 0.133354\n",
      "[491]\tvalid_0's l1: 0.133316\n",
      "[492]\tvalid_0's l1: 0.133248\n",
      "[493]\tvalid_0's l1: 0.133215\n",
      "[494]\tvalid_0's l1: 0.133182\n",
      "[495]\tvalid_0's l1: 0.133123\n",
      "[496]\tvalid_0's l1: 0.133105\n",
      "[497]\tvalid_0's l1: 0.133047\n",
      "[498]\tvalid_0's l1: 0.133012\n",
      "[499]\tvalid_0's l1: 0.13298\n",
      "[500]\tvalid_0's l1: 0.132953\n",
      "[501]\tvalid_0's l1: 0.132913\n",
      "[502]\tvalid_0's l1: 0.132851\n",
      "[503]\tvalid_0's l1: 0.132817\n",
      "[504]\tvalid_0's l1: 0.132777\n",
      "[505]\tvalid_0's l1: 0.132736\n",
      "[506]\tvalid_0's l1: 0.1327\n",
      "[507]\tvalid_0's l1: 0.132662\n",
      "[508]\tvalid_0's l1: 0.132598\n",
      "[509]\tvalid_0's l1: 0.13254\n",
      "[510]\tvalid_0's l1: 0.132502\n",
      "[511]\tvalid_0's l1: 0.132483\n",
      "[512]\tvalid_0's l1: 0.132453\n",
      "[513]\tvalid_0's l1: 0.132415\n",
      "[514]\tvalid_0's l1: 0.132367\n",
      "[515]\tvalid_0's l1: 0.132333\n",
      "[516]\tvalid_0's l1: 0.132296\n",
      "[517]\tvalid_0's l1: 0.132269\n",
      "[518]\tvalid_0's l1: 0.132243\n",
      "[519]\tvalid_0's l1: 0.132214\n",
      "[520]\tvalid_0's l1: 0.132195\n",
      "[521]\tvalid_0's l1: 0.132165\n",
      "[522]\tvalid_0's l1: 0.132122\n",
      "[523]\tvalid_0's l1: 0.132097\n",
      "[524]\tvalid_0's l1: 0.132059\n",
      "[525]\tvalid_0's l1: 0.132023\n",
      "[526]\tvalid_0's l1: 0.131988\n",
      "[527]\tvalid_0's l1: 0.131939\n",
      "[528]\tvalid_0's l1: 0.131913\n",
      "[529]\tvalid_0's l1: 0.131871\n",
      "[530]\tvalid_0's l1: 0.131797\n",
      "[531]\tvalid_0's l1: 0.131766\n",
      "[532]\tvalid_0's l1: 0.131739\n",
      "[533]\tvalid_0's l1: 0.131696\n",
      "[534]\tvalid_0's l1: 0.131664\n",
      "[535]\tvalid_0's l1: 0.131632\n",
      "[536]\tvalid_0's l1: 0.131602\n",
      "[537]\tvalid_0's l1: 0.131576\n",
      "[538]\tvalid_0's l1: 0.131525\n",
      "[539]\tvalid_0's l1: 0.131503\n",
      "[540]\tvalid_0's l1: 0.131466\n",
      "[541]\tvalid_0's l1: 0.131427\n",
      "[542]\tvalid_0's l1: 0.131391\n",
      "[543]\tvalid_0's l1: 0.131363\n",
      "[544]\tvalid_0's l1: 0.131343\n",
      "[545]\tvalid_0's l1: 0.1313\n",
      "[546]\tvalid_0's l1: 0.131242\n",
      "[547]\tvalid_0's l1: 0.131211\n",
      "[548]\tvalid_0's l1: 0.131188\n",
      "[549]\tvalid_0's l1: 0.131149\n",
      "[550]\tvalid_0's l1: 0.131126\n",
      "[551]\tvalid_0's l1: 0.131108\n",
      "[552]\tvalid_0's l1: 0.131072\n",
      "[553]\tvalid_0's l1: 0.131035\n",
      "[554]\tvalid_0's l1: 0.13101\n",
      "[555]\tvalid_0's l1: 0.13097\n",
      "[556]\tvalid_0's l1: 0.130942\n",
      "[557]\tvalid_0's l1: 0.130902\n",
      "[558]\tvalid_0's l1: 0.130845\n",
      "[559]\tvalid_0's l1: 0.130808\n",
      "[560]\tvalid_0's l1: 0.130789\n",
      "[561]\tvalid_0's l1: 0.13076\n",
      "[562]\tvalid_0's l1: 0.130737\n",
      "[563]\tvalid_0's l1: 0.130698\n",
      "[564]\tvalid_0's l1: 0.130661\n",
      "[565]\tvalid_0's l1: 0.130621\n",
      "[566]\tvalid_0's l1: 0.130578\n",
      "[567]\tvalid_0's l1: 0.130556\n",
      "[568]\tvalid_0's l1: 0.130513\n",
      "[569]\tvalid_0's l1: 0.1305\n",
      "[570]\tvalid_0's l1: 0.130468\n",
      "[571]\tvalid_0's l1: 0.130438\n",
      "[572]\tvalid_0's l1: 0.130402\n",
      "[573]\tvalid_0's l1: 0.130374\n",
      "[574]\tvalid_0's l1: 0.130345\n",
      "[575]\tvalid_0's l1: 0.130319\n",
      "[576]\tvalid_0's l1: 0.130296\n",
      "[577]\tvalid_0's l1: 0.130276\n",
      "[578]\tvalid_0's l1: 0.13025\n",
      "[579]\tvalid_0's l1: 0.130223\n",
      "[580]\tvalid_0's l1: 0.13018\n",
      "[581]\tvalid_0's l1: 0.130151\n",
      "[582]\tvalid_0's l1: 0.130105\n",
      "[583]\tvalid_0's l1: 0.130075\n",
      "[584]\tvalid_0's l1: 0.130049\n",
      "[585]\tvalid_0's l1: 0.130024\n",
      "[586]\tvalid_0's l1: 0.129989\n",
      "[587]\tvalid_0's l1: 0.129957\n",
      "[588]\tvalid_0's l1: 0.129925\n",
      "[589]\tvalid_0's l1: 0.129894\n",
      "[590]\tvalid_0's l1: 0.129867\n",
      "[591]\tvalid_0's l1: 0.129832\n",
      "[592]\tvalid_0's l1: 0.129804\n",
      "[593]\tvalid_0's l1: 0.129772\n",
      "[594]\tvalid_0's l1: 0.129755\n",
      "[595]\tvalid_0's l1: 0.129713\n",
      "[596]\tvalid_0's l1: 0.12967\n",
      "[597]\tvalid_0's l1: 0.129642\n",
      "[598]\tvalid_0's l1: 0.129613\n",
      "[599]\tvalid_0's l1: 0.129596\n",
      "[600]\tvalid_0's l1: 0.129554\n",
      "[601]\tvalid_0's l1: 0.129534\n",
      "[602]\tvalid_0's l1: 0.12951\n",
      "[603]\tvalid_0's l1: 0.129485\n",
      "[604]\tvalid_0's l1: 0.129465\n",
      "[605]\tvalid_0's l1: 0.129437\n",
      "[606]\tvalid_0's l1: 0.129395\n",
      "[607]\tvalid_0's l1: 0.129379\n",
      "[608]\tvalid_0's l1: 0.12934\n",
      "[609]\tvalid_0's l1: 0.129303\n",
      "[610]\tvalid_0's l1: 0.129283\n",
      "[611]\tvalid_0's l1: 0.129254\n",
      "[612]\tvalid_0's l1: 0.129236\n",
      "[613]\tvalid_0's l1: 0.129208\n",
      "[614]\tvalid_0's l1: 0.129193\n",
      "[615]\tvalid_0's l1: 0.129161\n",
      "[616]\tvalid_0's l1: 0.129128\n",
      "[617]\tvalid_0's l1: 0.129098\n",
      "[618]\tvalid_0's l1: 0.129081\n",
      "[619]\tvalid_0's l1: 0.129057\n",
      "[620]\tvalid_0's l1: 0.129043\n",
      "[621]\tvalid_0's l1: 0.129001\n",
      "[622]\tvalid_0's l1: 0.128963\n",
      "[623]\tvalid_0's l1: 0.128936\n",
      "[624]\tvalid_0's l1: 0.128915\n",
      "[625]\tvalid_0's l1: 0.128892\n",
      "[626]\tvalid_0's l1: 0.128867\n",
      "[627]\tvalid_0's l1: 0.128842\n",
      "[628]\tvalid_0's l1: 0.128827\n",
      "[629]\tvalid_0's l1: 0.128789\n",
      "[630]\tvalid_0's l1: 0.128775\n",
      "[631]\tvalid_0's l1: 0.128755\n",
      "[632]\tvalid_0's l1: 0.128717\n",
      "[633]\tvalid_0's l1: 0.128702\n",
      "[634]\tvalid_0's l1: 0.128673\n",
      "[635]\tvalid_0's l1: 0.128639\n",
      "[636]\tvalid_0's l1: 0.12862\n",
      "[637]\tvalid_0's l1: 0.128595\n",
      "[638]\tvalid_0's l1: 0.12858\n",
      "[639]\tvalid_0's l1: 0.128545\n",
      "[640]\tvalid_0's l1: 0.128527\n",
      "[641]\tvalid_0's l1: 0.128497\n",
      "[642]\tvalid_0's l1: 0.128473\n",
      "[643]\tvalid_0's l1: 0.128449\n",
      "[644]\tvalid_0's l1: 0.128432\n",
      "[645]\tvalid_0's l1: 0.128413\n",
      "[646]\tvalid_0's l1: 0.128387\n",
      "[647]\tvalid_0's l1: 0.128371\n",
      "[648]\tvalid_0's l1: 0.128354\n",
      "[649]\tvalid_0's l1: 0.128329\n",
      "[650]\tvalid_0's l1: 0.12831\n",
      "[651]\tvalid_0's l1: 0.12829\n",
      "[652]\tvalid_0's l1: 0.128268\n",
      "[653]\tvalid_0's l1: 0.12825\n",
      "[654]\tvalid_0's l1: 0.128216\n",
      "[655]\tvalid_0's l1: 0.128187\n",
      "[656]\tvalid_0's l1: 0.12817\n",
      "[657]\tvalid_0's l1: 0.128149\n",
      "[658]\tvalid_0's l1: 0.128128\n",
      "[659]\tvalid_0's l1: 0.128102\n",
      "[660]\tvalid_0's l1: 0.12805\n",
      "[661]\tvalid_0's l1: 0.128018\n",
      "[662]\tvalid_0's l1: 0.127999\n",
      "[663]\tvalid_0's l1: 0.127969\n",
      "[664]\tvalid_0's l1: 0.127945\n",
      "[665]\tvalid_0's l1: 0.127926\n",
      "[666]\tvalid_0's l1: 0.127903\n",
      "[667]\tvalid_0's l1: 0.127886\n",
      "[668]\tvalid_0's l1: 0.127875\n",
      "[669]\tvalid_0's l1: 0.127847\n",
      "[670]\tvalid_0's l1: 0.127815\n",
      "[671]\tvalid_0's l1: 0.127796\n",
      "[672]\tvalid_0's l1: 0.127763\n",
      "[673]\tvalid_0's l1: 0.12775\n",
      "[674]\tvalid_0's l1: 0.127714\n",
      "[675]\tvalid_0's l1: 0.127679\n",
      "[676]\tvalid_0's l1: 0.12765\n",
      "[677]\tvalid_0's l1: 0.127632\n",
      "[678]\tvalid_0's l1: 0.12762\n",
      "[679]\tvalid_0's l1: 0.12759\n",
      "[680]\tvalid_0's l1: 0.127545\n",
      "[681]\tvalid_0's l1: 0.127529\n",
      "[682]\tvalid_0's l1: 0.12751\n",
      "[683]\tvalid_0's l1: 0.127479\n",
      "[684]\tvalid_0's l1: 0.127451\n",
      "[685]\tvalid_0's l1: 0.127435\n",
      "[686]\tvalid_0's l1: 0.127413\n",
      "[687]\tvalid_0's l1: 0.127405\n",
      "[688]\tvalid_0's l1: 0.127381\n",
      "[689]\tvalid_0's l1: 0.127368\n",
      "[690]\tvalid_0's l1: 0.127353\n",
      "[691]\tvalid_0's l1: 0.127344\n",
      "[692]\tvalid_0's l1: 0.127322\n",
      "[693]\tvalid_0's l1: 0.127294\n",
      "[694]\tvalid_0's l1: 0.127263\n",
      "[695]\tvalid_0's l1: 0.127242\n",
      "[696]\tvalid_0's l1: 0.127216\n",
      "[697]\tvalid_0's l1: 0.127189\n",
      "[698]\tvalid_0's l1: 0.127176\n",
      "[699]\tvalid_0's l1: 0.127152\n",
      "[700]\tvalid_0's l1: 0.127136\n",
      "[701]\tvalid_0's l1: 0.12711\n",
      "[702]\tvalid_0's l1: 0.127091\n",
      "[703]\tvalid_0's l1: 0.127074\n",
      "[704]\tvalid_0's l1: 0.12705\n",
      "[705]\tvalid_0's l1: 0.127037\n",
      "[706]\tvalid_0's l1: 0.127024\n",
      "[707]\tvalid_0's l1: 0.127005\n",
      "[708]\tvalid_0's l1: 0.126994\n",
      "[709]\tvalid_0's l1: 0.126982\n",
      "[710]\tvalid_0's l1: 0.12696\n",
      "[711]\tvalid_0's l1: 0.126938\n",
      "[712]\tvalid_0's l1: 0.126908\n",
      "[713]\tvalid_0's l1: 0.126888\n",
      "[714]\tvalid_0's l1: 0.126877\n",
      "[715]\tvalid_0's l1: 0.126848\n",
      "[716]\tvalid_0's l1: 0.126824\n",
      "[717]\tvalid_0's l1: 0.126804\n",
      "[718]\tvalid_0's l1: 0.126786\n",
      "[719]\tvalid_0's l1: 0.126776\n",
      "[720]\tvalid_0's l1: 0.12675\n",
      "[721]\tvalid_0's l1: 0.126728\n",
      "[722]\tvalid_0's l1: 0.12671\n",
      "[723]\tvalid_0's l1: 0.126696\n",
      "[724]\tvalid_0's l1: 0.126658\n",
      "[725]\tvalid_0's l1: 0.12664\n",
      "[726]\tvalid_0's l1: 0.126624\n",
      "[727]\tvalid_0's l1: 0.126607\n",
      "[728]\tvalid_0's l1: 0.12658\n",
      "[729]\tvalid_0's l1: 0.126554\n",
      "[730]\tvalid_0's l1: 0.126524\n",
      "[731]\tvalid_0's l1: 0.126506\n",
      "[732]\tvalid_0's l1: 0.126496\n",
      "[733]\tvalid_0's l1: 0.126474\n",
      "[734]\tvalid_0's l1: 0.126448\n",
      "[735]\tvalid_0's l1: 0.126422\n",
      "[736]\tvalid_0's l1: 0.126404\n",
      "[737]\tvalid_0's l1: 0.126396\n",
      "[738]\tvalid_0's l1: 0.126378\n",
      "[739]\tvalid_0's l1: 0.126361\n",
      "[740]\tvalid_0's l1: 0.126334\n",
      "[741]\tvalid_0's l1: 0.126317\n",
      "[742]\tvalid_0's l1: 0.126288\n",
      "[743]\tvalid_0's l1: 0.126259\n",
      "[744]\tvalid_0's l1: 0.126239\n",
      "[745]\tvalid_0's l1: 0.126227\n",
      "[746]\tvalid_0's l1: 0.126203\n",
      "[747]\tvalid_0's l1: 0.12619\n",
      "[748]\tvalid_0's l1: 0.126164\n",
      "[749]\tvalid_0's l1: 0.12612\n",
      "[750]\tvalid_0's l1: 0.1261\n",
      "[751]\tvalid_0's l1: 0.126083\n",
      "[752]\tvalid_0's l1: 0.126061\n",
      "[753]\tvalid_0's l1: 0.12604\n",
      "[754]\tvalid_0's l1: 0.126021\n",
      "[755]\tvalid_0's l1: 0.126006\n",
      "[756]\tvalid_0's l1: 0.125987\n",
      "[757]\tvalid_0's l1: 0.125954\n",
      "[758]\tvalid_0's l1: 0.125946\n",
      "[759]\tvalid_0's l1: 0.125925\n",
      "[760]\tvalid_0's l1: 0.125917\n",
      "[761]\tvalid_0's l1: 0.125901\n",
      "[762]\tvalid_0's l1: 0.125885\n",
      "[763]\tvalid_0's l1: 0.125866\n",
      "[764]\tvalid_0's l1: 0.125836\n",
      "[765]\tvalid_0's l1: 0.125816\n",
      "[766]\tvalid_0's l1: 0.125799\n",
      "[767]\tvalid_0's l1: 0.125767\n",
      "[768]\tvalid_0's l1: 0.12574\n",
      "[769]\tvalid_0's l1: 0.125725\n",
      "[770]\tvalid_0's l1: 0.125704\n",
      "[771]\tvalid_0's l1: 0.125678\n",
      "[772]\tvalid_0's l1: 0.125658\n",
      "[773]\tvalid_0's l1: 0.125617\n",
      "[774]\tvalid_0's l1: 0.125592\n",
      "[775]\tvalid_0's l1: 0.125575\n",
      "[776]\tvalid_0's l1: 0.125562\n",
      "[777]\tvalid_0's l1: 0.125544\n",
      "[778]\tvalid_0's l1: 0.125519\n",
      "[779]\tvalid_0's l1: 0.125502\n",
      "[780]\tvalid_0's l1: 0.125481\n",
      "[781]\tvalid_0's l1: 0.125463\n",
      "[782]\tvalid_0's l1: 0.125449\n",
      "[783]\tvalid_0's l1: 0.125439\n",
      "[784]\tvalid_0's l1: 0.125408\n",
      "[785]\tvalid_0's l1: 0.125386\n",
      "[786]\tvalid_0's l1: 0.125369\n",
      "[787]\tvalid_0's l1: 0.125347\n",
      "[788]\tvalid_0's l1: 0.125329\n",
      "[789]\tvalid_0's l1: 0.12531\n",
      "[790]\tvalid_0's l1: 0.125299\n",
      "[791]\tvalid_0's l1: 0.125281\n",
      "[792]\tvalid_0's l1: 0.125271\n",
      "[793]\tvalid_0's l1: 0.125228\n",
      "[794]\tvalid_0's l1: 0.125216\n",
      "[795]\tvalid_0's l1: 0.1252\n",
      "[796]\tvalid_0's l1: 0.125184\n",
      "[797]\tvalid_0's l1: 0.125171\n",
      "[798]\tvalid_0's l1: 0.12516\n",
      "[799]\tvalid_0's l1: 0.125139\n",
      "[800]\tvalid_0's l1: 0.125126\n",
      "[801]\tvalid_0's l1: 0.125106\n",
      "[802]\tvalid_0's l1: 0.125086\n",
      "[803]\tvalid_0's l1: 0.125069\n",
      "[804]\tvalid_0's l1: 0.125055\n",
      "[805]\tvalid_0's l1: 0.125037\n",
      "[806]\tvalid_0's l1: 0.125024\n",
      "[807]\tvalid_0's l1: 0.125015\n",
      "[808]\tvalid_0's l1: 0.125005\n",
      "[809]\tvalid_0's l1: 0.124992\n",
      "[810]\tvalid_0's l1: 0.12498\n",
      "[811]\tvalid_0's l1: 0.124963\n",
      "[812]\tvalid_0's l1: 0.124939\n",
      "[813]\tvalid_0's l1: 0.124923\n",
      "[814]\tvalid_0's l1: 0.124901\n",
      "[815]\tvalid_0's l1: 0.124884\n",
      "[816]\tvalid_0's l1: 0.124857\n",
      "[817]\tvalid_0's l1: 0.124838\n",
      "[818]\tvalid_0's l1: 0.124825\n",
      "[819]\tvalid_0's l1: 0.124817\n",
      "[820]\tvalid_0's l1: 0.124804\n",
      "[821]\tvalid_0's l1: 0.124796\n",
      "[822]\tvalid_0's l1: 0.124782\n",
      "[823]\tvalid_0's l1: 0.124775\n",
      "[824]\tvalid_0's l1: 0.124744\n",
      "[825]\tvalid_0's l1: 0.124722\n",
      "[826]\tvalid_0's l1: 0.12471\n",
      "[827]\tvalid_0's l1: 0.124699\n",
      "[828]\tvalid_0's l1: 0.124679\n",
      "[829]\tvalid_0's l1: 0.124665\n",
      "[830]\tvalid_0's l1: 0.124642\n",
      "[831]\tvalid_0's l1: 0.124623\n",
      "[832]\tvalid_0's l1: 0.124605\n",
      "[833]\tvalid_0's l1: 0.124578\n",
      "[834]\tvalid_0's l1: 0.124564\n",
      "[835]\tvalid_0's l1: 0.12455\n",
      "[836]\tvalid_0's l1: 0.124533\n",
      "[837]\tvalid_0's l1: 0.12451\n",
      "[838]\tvalid_0's l1: 0.124497\n",
      "[839]\tvalid_0's l1: 0.124473\n",
      "[840]\tvalid_0's l1: 0.124435\n",
      "[841]\tvalid_0's l1: 0.124419\n",
      "[842]\tvalid_0's l1: 0.124401\n",
      "[843]\tvalid_0's l1: 0.124395\n",
      "[844]\tvalid_0's l1: 0.124387\n",
      "[845]\tvalid_0's l1: 0.124369\n",
      "[846]\tvalid_0's l1: 0.124356\n",
      "[847]\tvalid_0's l1: 0.124339\n",
      "[848]\tvalid_0's l1: 0.124324\n",
      "[849]\tvalid_0's l1: 0.124303\n",
      "[850]\tvalid_0's l1: 0.124289\n",
      "[851]\tvalid_0's l1: 0.124274\n",
      "[852]\tvalid_0's l1: 0.12426\n",
      "[853]\tvalid_0's l1: 0.124248\n",
      "[854]\tvalid_0's l1: 0.124234\n",
      "[855]\tvalid_0's l1: 0.124217\n",
      "[856]\tvalid_0's l1: 0.124202\n",
      "[857]\tvalid_0's l1: 0.124185\n",
      "[858]\tvalid_0's l1: 0.124166\n",
      "[859]\tvalid_0's l1: 0.124138\n",
      "[860]\tvalid_0's l1: 0.124124\n",
      "[861]\tvalid_0's l1: 0.124109\n",
      "[862]\tvalid_0's l1: 0.124099\n",
      "[863]\tvalid_0's l1: 0.124083\n",
      "[864]\tvalid_0's l1: 0.124074\n",
      "[865]\tvalid_0's l1: 0.124066\n",
      "[866]\tvalid_0's l1: 0.124049\n",
      "[867]\tvalid_0's l1: 0.124038\n",
      "[868]\tvalid_0's l1: 0.124027\n",
      "[869]\tvalid_0's l1: 0.124008\n",
      "[870]\tvalid_0's l1: 0.123987\n",
      "[871]\tvalid_0's l1: 0.123969\n",
      "[872]\tvalid_0's l1: 0.123948\n",
      "[873]\tvalid_0's l1: 0.123943\n",
      "[874]\tvalid_0's l1: 0.123903\n",
      "[875]\tvalid_0's l1: 0.123864\n",
      "[876]\tvalid_0's l1: 0.123845\n",
      "[877]\tvalid_0's l1: 0.123813\n",
      "[878]\tvalid_0's l1: 0.123802\n",
      "[879]\tvalid_0's l1: 0.123782\n",
      "[880]\tvalid_0's l1: 0.123769\n",
      "[881]\tvalid_0's l1: 0.123763\n",
      "[882]\tvalid_0's l1: 0.123749\n",
      "[883]\tvalid_0's l1: 0.123736\n",
      "[884]\tvalid_0's l1: 0.123724\n",
      "[885]\tvalid_0's l1: 0.123701\n",
      "[886]\tvalid_0's l1: 0.123688\n",
      "[887]\tvalid_0's l1: 0.123674\n",
      "[888]\tvalid_0's l1: 0.123652\n",
      "[889]\tvalid_0's l1: 0.123632\n",
      "[890]\tvalid_0's l1: 0.123622\n",
      "[891]\tvalid_0's l1: 0.123614\n",
      "[892]\tvalid_0's l1: 0.1236\n",
      "[893]\tvalid_0's l1: 0.12357\n",
      "[894]\tvalid_0's l1: 0.123556\n",
      "[895]\tvalid_0's l1: 0.123551\n",
      "[896]\tvalid_0's l1: 0.123541\n",
      "[897]\tvalid_0's l1: 0.123529\n",
      "[898]\tvalid_0's l1: 0.123514\n",
      "[899]\tvalid_0's l1: 0.123501\n",
      "[900]\tvalid_0's l1: 0.123486\n",
      "[901]\tvalid_0's l1: 0.123472\n",
      "[902]\tvalid_0's l1: 0.123461\n",
      "[903]\tvalid_0's l1: 0.123452\n",
      "[904]\tvalid_0's l1: 0.123441\n",
      "[905]\tvalid_0's l1: 0.12342\n",
      "[906]\tvalid_0's l1: 0.123406\n",
      "[907]\tvalid_0's l1: 0.123398\n",
      "[908]\tvalid_0's l1: 0.123385\n",
      "[909]\tvalid_0's l1: 0.12337\n",
      "[910]\tvalid_0's l1: 0.123349\n",
      "[911]\tvalid_0's l1: 0.123338\n",
      "[912]\tvalid_0's l1: 0.123318\n",
      "[913]\tvalid_0's l1: 0.123294\n",
      "[914]\tvalid_0's l1: 0.12328\n",
      "[915]\tvalid_0's l1: 0.123264\n",
      "[916]\tvalid_0's l1: 0.123255\n",
      "[917]\tvalid_0's l1: 0.123237\n",
      "[918]\tvalid_0's l1: 0.123231\n",
      "[919]\tvalid_0's l1: 0.123216\n",
      "[920]\tvalid_0's l1: 0.123207\n",
      "[921]\tvalid_0's l1: 0.123174\n",
      "[922]\tvalid_0's l1: 0.123167\n",
      "[923]\tvalid_0's l1: 0.123156\n",
      "[924]\tvalid_0's l1: 0.123147\n",
      "[925]\tvalid_0's l1: 0.123133\n",
      "[926]\tvalid_0's l1: 0.123118\n",
      "[927]\tvalid_0's l1: 0.123104\n",
      "[928]\tvalid_0's l1: 0.123094\n",
      "[929]\tvalid_0's l1: 0.123085\n",
      "[930]\tvalid_0's l1: 0.123073\n",
      "[931]\tvalid_0's l1: 0.123061\n",
      "[932]\tvalid_0's l1: 0.123052\n",
      "[933]\tvalid_0's l1: 0.123036\n",
      "[934]\tvalid_0's l1: 0.12303\n",
      "[935]\tvalid_0's l1: 0.123015\n",
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      "[937]\tvalid_0's l1: 0.122994\n",
      "[938]\tvalid_0's l1: 0.122982\n",
      "[939]\tvalid_0's l1: 0.122972\n",
      "[940]\tvalid_0's l1: 0.12296\n",
      "[941]\tvalid_0's l1: 0.12295\n",
      "[942]\tvalid_0's l1: 0.122935\n",
      "[943]\tvalid_0's l1: 0.122925\n",
      "[944]\tvalid_0's l1: 0.122886\n",
      "[945]\tvalid_0's l1: 0.122871\n",
      "[946]\tvalid_0's l1: 0.122862\n",
      "[947]\tvalid_0's l1: 0.122847\n",
      "[948]\tvalid_0's l1: 0.122837\n",
      "[949]\tvalid_0's l1: 0.122825\n",
      "[950]\tvalid_0's l1: 0.122815\n",
      "[951]\tvalid_0's l1: 0.122802\n",
      "[952]\tvalid_0's l1: 0.122791\n",
      "[953]\tvalid_0's l1: 0.122776\n",
      "[954]\tvalid_0's l1: 0.122768\n",
      "[955]\tvalid_0's l1: 0.122762\n",
      "[956]\tvalid_0's l1: 0.122755\n",
      "[957]\tvalid_0's l1: 0.122746\n",
      "[958]\tvalid_0's l1: 0.122726\n",
      "[959]\tvalid_0's l1: 0.122715\n",
      "[960]\tvalid_0's l1: 0.122706\n",
      "[961]\tvalid_0's l1: 0.122691\n",
      "[962]\tvalid_0's l1: 0.122677\n",
      "[963]\tvalid_0's l1: 0.122667\n",
      "[964]\tvalid_0's l1: 0.122659\n",
      "[965]\tvalid_0's l1: 0.122653\n",
      "[966]\tvalid_0's l1: 0.122642\n",
      "[967]\tvalid_0's l1: 0.122628\n",
      "[968]\tvalid_0's l1: 0.122617\n",
      "[969]\tvalid_0's l1: 0.122601\n",
      "[970]\tvalid_0's l1: 0.122597\n",
      "[971]\tvalid_0's l1: 0.122584\n",
      "[972]\tvalid_0's l1: 0.122574\n",
      "[973]\tvalid_0's l1: 0.122567\n",
      "[974]\tvalid_0's l1: 0.122557\n",
      "[975]\tvalid_0's l1: 0.122551\n",
      "[976]\tvalid_0's l1: 0.12253\n",
      "[977]\tvalid_0's l1: 0.122514\n",
      "[978]\tvalid_0's l1: 0.122509\n",
      "[979]\tvalid_0's l1: 0.122498\n",
      "[980]\tvalid_0's l1: 0.122489\n",
      "[981]\tvalid_0's l1: 0.122479\n",
      "[982]\tvalid_0's l1: 0.122466\n",
      "[983]\tvalid_0's l1: 0.122459\n",
      "[984]\tvalid_0's l1: 0.122449\n",
      "[985]\tvalid_0's l1: 0.122435\n",
      "[986]\tvalid_0's l1: 0.122418\n",
      "[987]\tvalid_0's l1: 0.122399\n",
      "[988]\tvalid_0's l1: 0.122381\n",
      "[989]\tvalid_0's l1: 0.122366\n",
      "[990]\tvalid_0's l1: 0.122345\n",
      "[991]\tvalid_0's l1: 0.122337\n",
      "[992]\tvalid_0's l1: 0.122319\n",
      "[993]\tvalid_0's l1: 0.122307\n",
      "[994]\tvalid_0's l1: 0.122297\n",
      "[995]\tvalid_0's l1: 0.122286\n",
      "[996]\tvalid_0's l1: 0.12227\n",
      "[997]\tvalid_0's l1: 0.122263\n",
      "[998]\tvalid_0's l1: 0.122254\n",
      "[999]\tvalid_0's l1: 0.12224\n",
      "[1000]\tvalid_0's l1: 0.122231\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\tvalid_0's l1: 0.122231\n",
      "[255 144   1  27   0 349  94 294 146 172 236 241   7 300 177   8 157 302\n",
      " 190 235]\n"
     ]
    }
   ],
   "source": [
    "from nni.algorithms.feature_engineering.gbdt_selector import GBDTSelector\n",
    "cat = {\n",
    "    'model': 'category',\n",
    "    'brand': 'category',\n",
    "    'bodyType': 'category',\n",
    "    'fuelType': 'category',\n",
    "    'gearbox': 'category',\n",
    "    'notRepairedDamage': 'category',\n",
    "    'regionCode': 'category',\n",
    "}\n",
    "\n",
    "df = pd.read_csv('../user_data/df.csv', sep=' ', dtype=cat)\n",
    "\n",
    "for i in range(15):\n",
    "    for j in range(i+1,15):\n",
    "        df['new'+str(i)+'*'+str(j)] = df['v_'+str(i)]*df['v_'+str(j)]\n",
    "\n",
    "\n",
    "#第二批特征工程\n",
    "for i in range(15):\n",
    "    for j in range(i+1,15):\n",
    "        df['new'+str(i)+'+'+str(j)] = df['v_'+str(i)]+df['v_'+str(j)]\n",
    "\n",
    "# 第三批特征工程\n",
    "for i in range(15):\n",
    "    df['new' + str(i) + '*power'] = df['v_' + str(i)] * df['power']\n",
    "\n",
    "\n",
    "#第四批特征工程\n",
    "for i in range(15):\n",
    "    for j in range(i+1,15):\n",
    "        df['new'+str(i)+'-'+str(j)] = df['v_'+str(i)]-df['v_'+str(j)]\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "五、筛选特征\n",
    "\"\"\"\n",
    "numerical_cols = df.select_dtypes(exclude='object').columns\n",
    "\n",
    "# initlize a selector\n",
    "fgs = GBDTSelector()\n",
    "# fit data\n",
    "param = {'boosting_type': 'gbdt',\n",
    "         'num_leaves': 55,  # 过小容易过拟合，过大则计算时间长\n",
    "         'max_depth': 35,\n",
    "         \"lambda_l2\": 2,  # 防止过拟合\n",
    "         'min_data_in_leaf': 20,  # 防止过拟合，好像都不用怎么调\n",
    "         'objective': 'regression_l1',\n",
    "         'learning_rate': 0.02,\n",
    "         \"min_child_samples\": 20,\n",
    "         'n_estimators': 1000,\n",
    "         \"feature_fraction\": 0.7,\n",
    "         \"bagging_freq\": 1,\n",
    "         \"bagging_fraction\": 0.9,\n",
    "         \"bagging_seed\": 11,\n",
    "         \"metric\": 'mae',\n",
    "         }\n",
    "train_X = df[df.train == 1].drop(['price', 'SaleID', 'regionCode'], axis=1)\n",
    "train_y = df[df.train == 1]['price']\n",
    "train_y_ln = np.log(train_y)\n",
    "fgs.fit(train_X,train_y_ln, lgb_params=param, eval_ratio=0.2,\n",
    "        early_stopping_rounds=300, importance_type='gain',\n",
    "        num_boost_round=1000\n",
    "        )\n",
    "print(fgs.get_selected_features(20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "gain = pd.DataFrame(fgs.feature_importance)\n",
    "gain['name'] = train_X.columns\n",
    "gain = gain.sort_values(0,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='name'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=60\n",
    "fig = plt.figure(figsize=(10, n/4))\n",
    "sns.barplot(x=0, y='name', data=gain.head(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[list(gain.iloc[:60].name)+['price', 'SaleID', 'regionCode', 'train']].to_csv('../user_data/df_s.csv', index=0, sep=' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../user_data/df_s.csv', sep=' ', dtype=cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['new3-0', 'new0-3', 'model', 'new0+12', 'age', 'regDate', 'new10-12',\n",
       "       'new0+14', 'new3*power', 'new12-10', 'new12+14', 'kilometer', 'new3+9',\n",
       "       'notRepairedDamage', 'new3+4', 'new6-3', 'new3-6', 'new1+12', 'new12+0',\n",
       "       'new12-3', 'new6+8', 'new5*12', 'new3-14', 'new3-12', 'new1+3',\n",
       "       'new12+13', 'new0+4', 'new4+12', 'new6*power', 'new1*power', 'new14-3',\n",
       "       'brand', 'new3*6', 'new10*power', 'new6*8', 'new6*12', 'new4+3',\n",
       "       'new2-3', 'new14+12', 'new14+0', 'new3-2', 'new14*power', 'v_8',\n",
       "       'new11+12', 'new0*8', 'new12*power', 'new0+13', 'new6*11', 'new10+12',\n",
       "       'new3-10', 'new3*12', 'new10-3', 'new14-1', 'new3-11', 'new1-12',\n",
       "       'new6*3', 'new8*10', 'new6*10', 'v_6', 'new1-14', 'price', 'SaleID',\n",
       "       'regionCode', 'train'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4\n",
       "1         0\n",
       "2         2\n",
       "3         5\n",
       "4         0\n",
       "         ..\n",
       "199995    4\n",
       "199996    6\n",
       "199997    1\n",
       "199998    8\n",
       "199999    1\n",
       "Name: SaleID, Length: 200000, dtype: category\n",
       "Categories (10, int64): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df.groupby(['regionCode'])['SaleID'].transform('count'), q=10,labels=range(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1046\n",
       "1         4366\n",
       "2         2806\n",
       "3          434\n",
       "4         6977\n",
       "          ... \n",
       "199995    5564\n",
       "199996    5220\n",
       "199997    3795\n",
       "199998      61\n",
       "199999    4158\n",
       "Name: regionCode, Length: 200000, dtype: category\n",
       "Categories (8010, object): ['0', '1', '10', '100', ..., '6873', '6995', '7807', '7996']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.regionCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('../user_data/df_s_cut.csv', index=0, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls=pd.read_csv('../prediction_result/submit.csv')\n",
    "cs = pd.read_csv('../prediction_result/submit_cgb.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SaleID</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000</td>\n",
       "      <td>1228.135565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>1989.547878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200002</td>\n",
       "      <td>8252.946848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>1087.069684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>1991.243806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>249995</td>\n",
       "      <td>6934.224560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>249996</td>\n",
       "      <td>18303.052578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>249997</td>\n",
       "      <td>5456.383210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>249998</td>\n",
       "      <td>4785.960336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>249999</td>\n",
       "      <td>5358.827805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SaleID         price\n",
       "0      200000   1228.135565\n",
       "1      200001   1989.547878\n",
       "2      200002   8252.946848\n",
       "3      200003   1087.069684\n",
       "4      200004   1991.243806\n",
       "...       ...           ...\n",
       "49995  249995   6934.224560\n",
       "49996  249996  18303.052578\n",
       "49997  249997   5456.383210\n",
       "49998  249998   4785.960336\n",
       "49999  249999   5358.827805\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SaleID</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000</td>\n",
       "      <td>1242.056188</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>1976.241700</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200002</td>\n",
       "      <td>8516.914823</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>1106.836699</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>2014.171441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>49995</th>\n",
       "      <td>249995</td>\n",
       "      <td>6895.288952</td>\n",
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       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>249996</td>\n",
       "      <td>18280.865862</td>\n",
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       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>249997</td>\n",
       "      <td>5438.004202</td>\n",
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       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>249998</td>\n",
       "      <td>4765.464525</td>\n",
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       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>249999</td>\n",
       "      <td>5307.889508</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SaleID         price\n",
       "0      200000   1242.056188\n",
       "1      200001   1976.241700\n",
       "2      200002   8516.914823\n",
       "3      200003   1106.836699\n",
       "4      200004   2014.171441\n",
       "...       ...           ...\n",
       "49995  249995   6895.288952\n",
       "49996  249996  18280.865862\n",
       "49997  249997   5438.004202\n",
       "49998  249998   4765.464525\n",
       "49999  249999   5307.889508\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SaleID</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>5843.761769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14433.901067</td>\n",
       "      <td>7374.224925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>13.777427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>212499.750000</td>\n",
       "      <td>1312.624435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>3205.210600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>237499.250000</td>\n",
       "      <td>7577.993904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>127900.325516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SaleID          price\n",
       "count   50000.000000   50000.000000\n",
       "mean   224999.500000    5843.761769\n",
       "std     14433.901067    7374.224925\n",
       "min    200000.000000      13.777427\n",
       "25%    212499.750000    1312.624435\n",
       "50%    224999.500000    3205.210600\n",
       "75%    237499.250000    7577.993904\n",
       "max    249999.000000  127900.325516"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "cs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SaleID</th>\n",
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       "      <td>50000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>5862.969037</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14433.901067</td>\n",
       "      <td>7364.246917</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>14.467031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>212499.750000</td>\n",
       "      <td>1324.507496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>3211.481966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>237499.250000</td>\n",
       "      <td>7594.886722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>94855.647197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SaleID         price\n",
       "count   50000.000000  50000.000000\n",
       "mean   224999.500000   5862.969037\n",
       "std     14433.901067   7364.246917\n",
       "min    200000.000000     14.467031\n",
       "25%    212499.750000   1324.507496\n",
       "50%    224999.500000   3211.481966\n",
       "75%    237499.250000   7594.886722\n",
       "max    249999.000000  94855.647197"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs=pd.DataFrame()\n",
    "fs['SaleID']=ls.SaleID\n",
    "fs['price']=(cs.price+ls.price)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>8384.930836</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>1096.953192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>2002.707623</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>249995</td>\n",
       "      <td>6914.756756</td>\n",
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       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>249996</td>\n",
       "      <td>18291.959220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>249997</td>\n",
       "      <td>5447.193706</td>\n",
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       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>249998</td>\n",
       "      <td>4775.712431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>249999</td>\n",
       "      <td>5333.358656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SaleID         price\n",
       "0      200000   1235.095876\n",
       "1      200001   1982.894789\n",
       "2      200002   8384.930836\n",
       "3      200003   1096.953192\n",
       "4      200004   2002.707623\n",
       "...       ...           ...\n",
       "49995  249995   6914.756756\n",
       "49996  249996  18291.959220\n",
       "49997  249997   5447.193706\n",
       "49998  249998   4775.712431\n",
       "49999  249999   5333.358656\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SaleID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>5853.365403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14433.901067</td>\n",
       "      <td>7364.689096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>14.259331</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>212499.750000</td>\n",
       "      <td>1317.248412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224999.500000</td>\n",
       "      <td>3208.260483</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>237499.250000</td>\n",
       "      <td>7594.542526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>100787.403097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SaleID          price\n",
       "count   50000.000000   50000.000000\n",
       "mean   224999.500000    5853.365403\n",
       "std     14433.901067    7364.689096\n",
       "min    200000.000000      14.259331\n",
       "25%    212499.750000    1317.248412\n",
       "50%    224999.500000    3208.260483\n",
       "75%    237499.250000    7594.542526\n",
       "max    249999.000000  100787.403097"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs.to_csv('../prediction_result/submit_fusion.csv',index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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